Getting Started with PyTorch TorchTune: A Step-by-Step Tutorial

Welcome to the ultimate guide on getting started with PyTorch TorchTune! If you're a developer looking to fine-tune large language models (LLMs) with ease, you've come to the right place. TorchTune is a PyTorch-native library designed to simplify the process of authoring, fine-tuning, and experimenting with LLMs. In this comprehensive tutorial, we'll walk you through everything you need to know to get started, from installation to running your first fine-tuning job.

What is TorchTune?

TorchTune is a PyTorch library that emphasizes simplicity, extensibility, correctness, and stability. It provides modular, native-PyTorch implementations of popular LLMs, training recipes for various fine-tuning techniques, and seamless integration with popular tools like Hugging Face Datasets and EleutherAI's Eval Harness.

Key Features

• Simplicity and Extensibility: Native-PyTorch, componentized design, and easy-to-reuse abstractions.
• Correctness: High bar on proving the correctness of components and recipes.
• Stability: PyTorch just works, and so should TorchTune.
• Democratizing LLM Fine-Tuning: Works out-of-the-box on different hardware setups.

Installation

Before we dive into the nitty-gritty, let's get TorchTune installed on your machine.

Step 1: Install PyTorch

TorchTune is tested with the latest stable PyTorch release as well as the preview nightly version. For fine-tuning multimodal LLMs, you'll also need to install torchvision.

# Install stable version of PyTorch using pip
pip install torch torchvision
 
# Nightly install for latest features
pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu121

Step 2: Install TorchTune

The latest stable version of TorchTune is hosted on PyPI and can be downloaded with the following command:

pip install torchtune

To confirm that the package is installed correctly, you can run the following command:

tune --help

You should see the following output:

usage: tune [-h] {ls,cp,download,run,validate} ...
 
Welcome to the torchtune CLI!
 
options:
  -h, --help            show this help message and exit
 
...

Downloading a Model

TorchTune integrates seamlessly with the Hugging Face Hub, making it easy to download pre-trained models. For this tutorial, we'll use the Llama2 7B model from Meta.

Step 1: Get Access to the Model

Follow the instructions on the official Meta Llama page to gain access to the model. Once you have access, you can download the model using the following command:

tune download meta-llama/Llama-2-7b-hf \\
  --output-dir /tmp/Llama-2-7b-hf \\
  --hf-token <ACCESS TOKEN>

Step 2: Verify the Download

Ensure that the model files are downloaded correctly by checking the output directory.

Fine-Tuning Your First Model

Now that we have the model, let's fine-tune it using TorchTune. We'll use the LoRA (Low-Rank Adaptation) technique for this tutorial.

Step 1: Select a Recipe

TorchTune provides built-in recipes for various fine-tuning techniques. You can list all available recipes using the following command:

tune ls

For this tutorial, we'll use the llama2/7B_lora_single_device recipe.

Step 2: Modify the Config

You can override existing parameters from the command line or copy the config to your local directory for more substantial changes.

Command Line Override

tune run lora_finetune_single_device --config llama2/7B_lora_single_device epochs=1

Copy and Modify Config

tune cp llama2/7B_lora_single_device custom_config.yaml

Now, you can update the custom YAML config any way you like.

Step 3: Run the Fine-Tuning Job

With the config set up, you can now run the fine-tuning job:

tune run lora_finetune_single_device --config llama2/7B_lora_single_device epochs=1

You should see logs indicating that the model has started training.

Evaluating Your Fine-Tuned Model

Once the model is fine-tuned, it's essential to evaluate its performance. TorchTune integrates with EleutherAI's evaluation harness for this purpose.

Step 1: Install the Evaluation Harness

pip install \"lm_eval==0.4.*\"

Step 2: Run Evaluation

Copy the evaluation config and modify it to point to your fine-tuned checkpoints.

tune cp eleuther_evaluation ./custom_eval_config.yaml

Update custom_eval_config.yaml to include the fine-tuned checkpoints and run the evaluation:

tune run eleuther_eval --config ./custom_eval_config.yaml

Generating Text with Your Fine-Tuned Model

Finally, let's generate some text to see how well the model performs in real-world scenarios.

Step 1: Copy the Generation Config

tune cp generation ./custom_generation_config.yaml

Step 2: Modify the Config

Update custom_generation_config.yaml to point to your checkpoint and tokenizer.

Step 3: Run Generation

tune run generate --config ./custom_generation_config.yaml \\
  prompt=\"What are some interesting sites to visit in the Bay Area?\"

You should see the generated text in the logs.

Speeding Up Generation with Quantization

To speed up inference, you can use quantization techniques provided by TorchTune.

Step 1: Copy the Quantization Config

tune cp quantization ./custom_quantization_config.yaml

Step 2: Modify the Config

Update custom_quantization_config.yaml to include the checkpoint files.

Step 3: Run Quantization

tune run quantize --config ./custom_quantization_config.yaml

Step 4: Re-run Generation

Update custom_generation_config.yaml to point to the quantized model and run the generation command again.

Conclusion

Congratulations! You've successfully fine-tuned, evaluated, and generated text with your LLM using TorchTune. This comprehensive guide should have given you a solid foundation to start experimenting with TorchTune in your projects.

For more personalized code and up-to-date answers to your questions, don't forget to use the AI Assist at Torchtune Agent. This AI expert is trained on trusted sources for TorchTune and can help you with any queries you might have.

Happy fine-tuning!